Tuning Derivatives for Causal Fairness in Machine Learning
Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age. Classical fairness notions, most notably Statistical Parity (SP), demand that predictions be independent of the protected attributes, but are overly restrictive when these attributes influence mediating variables that are considered business necessities. Recent causal formulations relax SP by distinguishing allowed from not-allowed causal paths and by complementing SP with Predictive Parity (PP), requiring the predictor to replicate the legitimate influence of business-necessities. Existing path-based definiti...
| Authors | Filip Edström, Guilherme W. F. Barros, Tetiana Gorbach et al. |
|---|---|
| Primary category | stat.ML |
| Published | 7 May 2026 |
| Last updated at source | 10 July 2026 |
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